The second largest category of drift detection algorithms is data distribution-based drift detection. In this category of algorithms, the dissimilarity between the distribution of new data and historical data is quantified by using a distance metric. Once the dissimilarity is proven to be statistically significantly different, a drift alarm will be triggered. According to the literature, the first formal treatment of change detection in data streams was proposed by [1]}. Another typical data distribution-based drift detection algorithm is the Information-Theoretic Approach (ITA) [2]}. Similar implementations have been adopted in the Competence Model-based drift detection (CM) [3]}, Equal Density Estimation (EDE) [4]}, and Local Drift Degree-based Density Synchronized Drift Adaptation (LDD-DSDA) [5]}.
